Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	21,889
2	New York	21,302
3	Arizona	21,024
4	New Jersey	20,243
5	Florida	18,151
6	Rhode Island	17,131
7	Massachusetts	16,633
8	District of Columbia	16,395
9	Mississippi	16,146
10	Alabama	15,135
11	South Carolina	14,878
12	Delaware	14,299
13	Georgia	13,549
14	Connecticut	13,528
15	Maryland	13,463
16	Illinois	13,318
17	Nevada	13,014
18	Texas	12,970
19	Iowa	12,930
20	Tennessee	12,395
21	Nebraska	12,312
22	Arkansas	12,015
23	Utah	11,284
24	California	10,963
25	North Carolina	10,215
26	Virginia	9,517
27	Idaho	9,435
28	South Dakota	9,204
29	Indiana	9,072
30	New Mexico	8,662
31	Minnesota	8,646
32	Wisconsin	8,544
33	Pennsylvania	8,526
34	Michigan	8,469
35	Kansas	8,415
36	Colorado	7,364
37	North Dakota	7,213
38	Oklahoma	7,068
39	Washington	6,862
40	Ohio	6,859
41	Missouri	6,504
42	Kentucky	5,781
43	New Hampshire	4,646
44	Puerto Rico	4,218
45	Wyoming	4,055
46	Oregon	3,737
47	Alaska	3,666
48	West Virginia	3,096
49	Maine	2,780
50	Montana	2,735
51	Vermont	2,206
52	Hawaii	1,036

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Louisiana	484
2	Mississippi	466
3	Florida	457
4	Alabama	361
5	Nevada	355
6	Arizona	353
7	Texas	349
8	Georgia	347
9	South Carolina	334
10	Tennessee	327
11	California	278
12	Idaho	276
13	Arkansas	257
14	Oklahoma	213
15	Missouri	210
16	North Carolina	186
17	Utah	171
18	Wisconsin	171
19	Nebraska	167
20	North Dakota	160
21	Iowa	153
22	New Mexico	150
23	Kentucky	138
24	Alaska	123
25	Indiana	122
26	Maryland	118
27	Ohio	114
28	Kansas	111
29	Virginia	111
30	Illinois	110
31	District of Columbia	109
32	Puerto Rico	105
33	Delaware	102
34	Colorado	101
35	Washington	100
36	Minnesota	95
37	Wyoming	92
38	Montana	88
39	Rhode Island	76
40	South Dakota	75
41	West Virginia	75
42	Michigan	70
43	Pennsylvania	69
44	Oregon	67
45	Massachusetts	41
46	New York	40
47	New Jersey	32
48	Hawaii	22
49	Connecticut	16
50	New Hampshire	16
51	Vermont	9
52	Maine	6

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,770
2	New York	1,658
3	Connecticut	1,236
4	Massachusetts	1,230
5	Rhode Island	944
6	District of Columbia	823
7	Louisiana	792
8	Michigan	640
9	Illinois	596
10	Maryland	563
11	Pennsylvania	557
12	Delaware	543
13	Mississippi	482
14	Indiana	427
15	Arizona	421
16	Colorado	310
17	Georgia	309
18	New Hampshire	297
19	Alabama	284
20	New Mexico	284
21	Minnesota	283
22	Ohio	278
23	Iowa	259
24	South Carolina	259
25	Florida	256
26	Virginia	240
27	Nevada	230
28	California	207
29	Washington	203
30	Missouri	197
31	North Carolina	167
32	Nebraska	164
33	Texas	162
34	Kentucky	158
35	Wisconsin	152
36	South Dakota	136
37	Tennessee	133
38	North Dakota	132
39	Arkansas	127
40	Oklahoma	120
41	Kansas	111
42	Vermont	89
43	Maine	87
44	Utah	84
45	Idaho	79
46	Oregon	65
47	Puerto Rico	58
48	West Virginia	57
49	Wyoming	43
50	Montana	40
51	Alaska	23
52	Hawaii	17

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	12
2	South Carolina	11
3	Louisiana	8
4	Mississippi	8
5	Alabama	7
6	Florida	6
7	Nevada	6
8	Texas	6
9	Georgia	5
10	California	3
11	Idaho	3
12	Tennessee	3
13	Arkansas	2
14	Delaware	2
15	Indiana	2
16	Iowa	2
17	Massachusetts	2
18	Missouri	2
19	New Mexico	2
20	North Carolina	2
21	Oklahoma	2
22	Utah	2
23	Colorado	1
24	Illinois	1
25	Maryland	1
26	Montana	1
27	New Hampshire	1
28	New York	1
29	North Dakota	1
30	Ohio	1
31	Oregon	1
32	Pennsylvania	1
33	Rhode Island	1
34	South Dakota	1
35	Washington	1
36	Wisconsin	1
37	Alaska	0
38	Connecticut	0
39	District of Columbia	0
40	Hawaii	0
41	Kansas	0
42	Kentucky	0
43	Maine	0
44	Michigan	0
45	Minnesota	0
46	Nebraska	0
47	New Jersey	0
48	Puerto Rico	0
49	Vermont	0
50	Virginia	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	137,894	1	99
Lake	Tennessee	100,200	2	99
Lee	Arkansas	98,002	3	99
Dakota	Nebraska	93,878	4	99
Buena Vista	Iowa	90,010	5	99
Davidson	Tennessee	27,127	118	96
Richland	South Carolina	16,084	370	88
Orange	California	10,281	796	74
York	South Carolina	9,492	884	71
Pierce	Washington	5,066	1559	50

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,902	1	99
Randolph	Georgia	3,836	2	99
Terrell	Georgia	3,399	3	99
Early	Georgia	3,042	4	99
McKinley	New Mexico	2,929	5	99
Richland	South Carolina	293	628	80
Davidson	Tennessee	258	705	77
Orange	California	171	945	69
Pierce	Washington	136	1098	65
York	South Carolina	64	1591	49

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.